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Application of Multi Criteria Decision Analysis for Selection of Best Alternative Process and Coagulation-Flocculation Operation  in Pre-Treatment Water System of Steam Power Plant

Diposting oleh On Monday, May 11, 2020


Application of Multi Criteria Decision Analysis for Selection of Best Alternative Process and Coagulation-Flocculation Operation
in Pre-Treatment Water System of Steam Power Plant
Yuni Eko Feriyanto1,a, Udisubakti Ciptomulyono2,b and Endah Angreni3,c
1,2,3Department of Business and Technology Management, Institute of Technology Sepuluh Nopember Surabaya, Indonesia
aYE.Feriyanto@gmail.com, bUdisubakti@gmail.com, cAngreni_bums@yahoo.com
Keywords: Coagulation-Flocculation, Jar test, Multicriteria decision analysis, Steam power plant, Water treatment.
Abstract. Water Treatment Plant (WTP) is one of the system stages in steam power plant which includes a series of sub-system processes such as screening, pre-treatment water, desalination and demineralization. At the pre-treatment stage of the water system, there is a sludge deposition process that is assisted with the chemical coagulant and coagulant-aid. The most common problem in steam power plant water treatment is despite the plentiful dose used in the rainy season and sea tidal condition, the quality of the water produced still does not meet the standard of steam power plant. The proposed jar-test technique was performed using process and operation variables that potentially affect the performance of coagulant-coagulan aid such as the % dosage, residence time and agitator cycle. The water quality was measured using criteria such as turbidity, conductivity, pH, TSS and TDS. In this papaer, the use of multicriteria decision analysis (MCDA) was proposed using two approaches of AHP and AHP-TOPSIS methods. The sensitivities of both methods were analyzed and the decision model obtained was subsequently discussed and compared. Based on the result of this paper, it can be concluded that the best alternative process and operation coagulation-flocculation in steam power plant pre-treatment water system based on the acquisition of decision models for two different methods was the first alternative for D60W30P80 and the second alternative for D40W20P80.
Introduction
     Physical-chemical treatment in aqueous settling basin is a treatment of reduction in sludge content carried by feed water and is known as the coagulation-flocculation system. This research took the subject of feed water treatment at steam power plant from sea water. According to Table 1, the quality of sea water entering the steam power plant water treatment varied and depended on seasons and tides.
Table 1. Quality of Feed Water in Two Different Seasons
Parameter
Unit
Measuring Value
Rain Season
Dry Season
Turbidity
NTU
30-40
6.6-8.5
Conductivity
µS/cm
± 47,000
± 47,000
pH
value
± 8
± 8
TSS
mg/L
± 30
± 20
     The routine operational cost structure of the steam power plant is divided into two, namely operating cost which includes water treatment, steam treatment and operation of human resources management while maintenance cost which includes replacement of spare parts, predictive maintenance and maintenance of human resources management. Based on these two costs, there are issues of high operational costs such as replacement of spare parts for maintenance scope and water treatment for operation scope.
     WTP consists of several series of systems and each system produces a product that becomes the next feed water for the system so that the resulting feed water quality standards are expected to meet steam power plant standards. The high operational cost is caused by the impact of the chain system. Therefore, it is important to focus in improving the early stage of WTP system which is pre-treatment water system to solve the problem. Pre-treatment water system in steam power plant is coagulation-flocculation system. The most common problem in this system is that the dosage requirement during the rainy season and high tide is high compared to the dry season, however the water quality of the system output still does not meet the standard of steam power plant. Thus, a deep study to analyze the cause of this phenomenon is needed. The result of sampling output of coagulation-flocculation system in rainy season is described in Table 2.
Table 2. The Comparison Between Real Water Quality on Rainy Season and Standard
Unit
Measuring Value
Real
Standard
Turbidity
NTU
8,43
<5
Conductivity
μS/cm
48.700
<48.900
pH
Nilai
7,497
7-8
TSS
mg/L
10
<10
TDS
g/L
24,2
<24,2
     Based on Table 2, it was found that there were 3 of the 5 criteria that not met the standards of steam power plants at the sampling measurements of coagulation-flocculation products in the rainy season. These criteria were turbidity, TSS and TDS. The effectiveness of coagulation-flocculation process was supported by optimal dosage as well as appropriate processes and operations such as % dosage, residence time and agitator cycle [1]. Several parameters of water quality such as turbidity, conductivity, pH, TSS and TDS were used to know the effectiveness of coagulation-flocculation [1, 2, 3]. The study used several water quality parameters to find the right type of coagulant aid using multicriteria decision analysis system [4].
     The jar-test technique is used for laboratory-scale experiments with the problem taken in such a way similar to the actual conditions in the field. The principle of jar-test is to conduct repetitive experiments with various variables so that the information about variables selection that produce good measurable criteria. Previous studies of coagulations-flocculation was studies that used standardized process and operation variables such as %dosage, residence time and agitator cycle [3]. Jar-test result is data criteria with combination process and operation variables of coagulation-flocculation as alternatives.
     The selection of the best alternative is difficult to perform because in the decision-making system, it is preferred to ensure the achievement of decision through a series of activities that analyze the alternative decision solutions, the parameters and constraints that exist and then choose the best rather than choose the right choice first and immediately [5]. The selection of the best combinations of coagulation-flocculation which involve lot of criteria in measurements often encounters conflicting situations such as alternatives which likely to be accepted in turbidity and TSS reduction but still rejected because of the impact on the increase of conductivity and TDS.
     Based on such conditions, it is proposed to use multicriteria decision analysis (MCDA) method which can be used to accommodate alternative selection with multicriteria consideration. Selection of MCDA type is difficult because each type has advantages and disadvantages. The election is also based on the structure of the problem, the objectives to be achieved and the existence of constraints if there is any. In general, many researchers combine MCDA types to complement the deficiencies of each MCDA type in priority ranking determinations such as AHP-Preference Ranking Organization Methods for Enrichment Evaluations (PROMETHEEs) [2], AHP-Elimination Et Choix Tradnisant La Realite (ELECTRE) [6], AHP-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) [7]. In the priority ranking determination, the AHP method approach is used for weighting criteria by expert judgment based on the relative importance level so that the decision alternative can provide satisfaction for the decision maker according to the desired aspiration level and believe in the process [5].
Method
Tools and Materials. The tool used for this experiment was a jar-test kit, beaker glass, plastic type sample bottle, 10-100 μL analytic pipette volume, digital TSS meter, analytic balance, digital TDS meters, digital pH meter, digital conductivity meter and digital turbidity meter. The materials used were sea water, demineralized water, aluminum hydroxychloride type of coagulant and anionic poly-acrylamide type of coagulant-aid.
     Jar Test. The jar-test in this paper uses 6 paddle motors with 1 liter beaker glass with the following experimental procedures: (i) placing sea water in beaker glass then measuring water quality parameters prior to coagulation-flocculation treatment; (ii) adjusting the agitator cycle at 150 rpm accompanied by coagulant affixing and reacting for 0.5 min; (iii) reducing agitator cycle according to variable that was 40/60/80 rpm accompanied by coagulant-aid with residence time of suspended solid binding according to variable that was 10/20/30/40 min; (iv) the end of experiment was to let the sample for ± 5 minutes and to measure water quality by taking water sample ± 2 cm from surface; (v) measuring water quality using criterion variable to obtain water quality data after coagulation-flocculation treatment.
     Initial Data Processing. Based on the jar-test results data, the alternatives obtained were 48 pieces and the water quality of each alternative measured before and after the coagulation-flocculation process. Out of the 48 alternatives, the equalization of criteria unit was performed with calculation of %increase/decrease of water quality value. The calculation of %increase/decrease of water quality value was performed to obtain real data in the field that present 100% dosage and jar-test result data for variable according to dose. The next stage was the selection of alternative decision by selecting data with better criteria or equal to real data in the field then 14 alternative decisions were obtained. The results of this selection were arranged to Table 3 which was used for data processing materials.
Table 3. Data of Decision Alternative Selection Results
Alternative Symbol
% Increase/Decrease of Operational Parameter
Turbidity
Conductivity
pH
TSS
TDS
D40W10P60
67,1
0,63
1,50
39,4
0,42
D40W20P80
81,4
0,21
0,45
63,9
0,42
D40W30P40
72,7
0,21
1,05
60,0
0,42
D40W30P80
80,1
0,42
1,00
40,0
0,42
D40W40P40
70,7
0,00
1,57
56,1
0,00
D40W40P80
76,7
0,21
1,47
30,0
0,42
D60W10P60
76,1
0,42
1,24
40,6
0,42
D60W10P80
71,5
0,21
0,38
34,4
0,42
D60W20P60
67,6
0,00
0,19
29,0
0,00
D60W20P80
82,9
0,21
0,14
43,8
0,42
D60W30P80
80,4
0,84
0,97
62,9
0,00
D80W10P60
76.2
0,00
0,71
53,1
0,00
D80W10P80
74.6
0,00
0,28
54,1
0,00
D80W20P80
69.9
0,21
0,25
45,0
0,42
Problems can be structured  mathematically according to Fig. 1.           
Problems can be structured  mathematically according to Fig. 1.
Fig. 1. The Decision Hierarchy
Based on the Fig. 1, the decision hierarchy structure was obtained by the following information: (i) the research objective was the selection of the best alternative of coagulation-flocculation process and operation; (ii) criteria were measurable parameters for water quality such as turbidity, conductivity, pH, TSS and TDS; (iii) the alternative was a combination of coagulation-flocculation processes and operations such as %dose (D) i.e 20/40/60/80%; residence time (W) i.e 10/20/30/40 min and agitator cycle i.e 40/60/80 rpm.
The Application of MCDA Method. The MCDA method is an alternative selection process method for obtaining the optimal solution of some decision alternatives by taking criteria or objectives that are more than one in conflicting situations into account [5]. In this paper, the AHP method and AHP-TOPSIS approach were proposed. The AHP method is measurement theory with pairwaise comparisons and is based on expert decisions to arrange the priority scale [8]. In solving multicriteria problems, the AHP method was used to obtain priority based on the decision maker's preference assessment by pairwise comparison representing the essential ability of humans to develop their perceptions gradually, comparing a pair of equivalent solutions to the given criteria [9]. In the AHP method, a relative scale of interest with a Saaty scale of 1-9 was used and performed by expert judgment. The form of the calculation of this method was the decision matrix and system consistency ratio (CR) involving component consistency index (CI) and random index (RI) was used in the consistency calculation. Calculations  was performed using the Eq. 1 and the Eq. 2.
The AHP method is generally used extensively by previous researchers such as the selection of the optimal technology to rehabilitate the pipes in water distribution system [10], application for reinforcement of hydropower strategy [11]. The AHP method according to previous researchers lacks in the ranking system so that the combination with other MCDA methods is required for the improvement of one of TOPSIS [12]. Although TOPSIS uses the concept of a popular and simple method, it often gets input because of its inability in providing space for an uncertainty and perception for decision makers [13]. To overcome this deficiency, we used a combined AHP-TOPSIS method with principle of using expert judgment perception in uncertainty criteria assessment. Previous research for this method were selection of development projects for oilfields [7], and selection of sustainable supplier countries for the steel industry [14]. TOPSIS proposed by Hwang and Yoon (1981) used to determine the positive ideal solution and the negative ideal solution. The best alternative selection was the data that had the shortest distance from the positive ideal solution and the furthest distance from the ideal negative solution [15]. Here are the steps of the AHP-TOPSIS method [16].
Step 1. Compiling a normalized decision matrix
Step 2. Arranging the weight of the normalized decision matrix
Step 3. Determining the positive and negative ideal solution
Step 4. Calculating the Euclidean distance between the positive and negative ideal solutions for each variable
Step 5. Calculating the relative closeness to a positive ideal solution for each alternative
Results and Discussion
Criteria Weighting. The criteria selected were based on standard steam power plant manuals book and studies that had been conducted by previous researchers [1, 2, 3]. The criteria weighting was proposed using AHP method approach with decision maker by expert judgment according to qualification which had been determined because this method was commonly used by previous researcher and its simple calculation [2]. The criteria weighting scoring system was performed by expert judgment using a pairwise scale system of Saaty 1-9 and continued by calculating it using expert choice v11 (EC 11) software assistance to obtain the criteria weight as shown in Fig. 2
Fig. 2. Criteria Weight by Expert Judgment using EC 11 Software
Based on the expert's judgment, turbidity criteria had the highest priority ranking followed by TSS, while other criteria such as conductivity, TDS and pH were determined to have less effect on water quality.
Priority Ranking Selection
Approach of AHP Method. The AHP method in this paper was proposed and used for the determination of criteria weighting and priority ranking determination to select the best process and operation of coagulation-flocculation due to the simplicity and easy calculation based on expert judgment assessment. Initial stages of data to be calculated in this paper was the scoring process with reference assessment in accordance with the provisions set by the expert judgment in his knowledge in the water treatment system of the steam power plant. The results data are presented in Table 4.
Table 4. Decision Alternative Matrix by AHP Method Approach
Alternative Variables
Scoring Results
Turbidity
0.433(a)
Conductivity
0.097(a)
pH
0.034(a)
TSS
0.353(a)
TDS
0.084(a)
D40W10P60
2
9
3
4
9
D40W20P80
9
6
2
9
9
D40W30P40
5
9
2
9
9
D40W30P80
9
9
4
4
9
D40W40P40
4
9
3
8
2
D40W40P80
7
9
4
2
9
D60W10P60
7
9
3
4
9
D60W10P80
4
5
3
3
9
D60W20P60
2
3
2
2
2
D60W20P80
9
3
3
5
9
D60W30P80
9
9
4
9
2
D80W10P60
7
9
2
8
2
D80W10P80
6
4
2
8
2
D80W20P80
3
4
2
6
9
(a)    Criteria Weight
The alternative decision matrix with the scoring system were arrenged and presented, then it was subsequently calculated to determine the priority ranking using the EC 11 software. The result data could be seen in Fig. 3.
Fig. 3. Priority Ranking by AHP Method Approach using EC 11 Software
Based on the data obtained in Fig. 3, the priority ranking for 14 alternatives was presented and this result was still based on the weight performed by the expert judgment so that if there is a change of decision, the level of consistency rank could not be determined yet.
Sensitivity Analysis of AHP Method. The weight of the criteria has a significant influence on the priority ranking sequence. Decision makers may at any time change the provisions that affect the decisions. Therefore, sensitivity analysis is recommended to use with the principle of altering the weighting criteria with the assistance of EC 11 software until there is a significant level of priority ranking changes generated [14]. The proposed sensitivity analysis used was the weighting of the criteria by +10%, +20% and -10% for turbidity criteria and TSS as two top priority ranking. As for the sensitivity of +10%, +20% and -10% turbidity, the result obtained were the rank 1 to 3 of the standard AHP method did not change the order of priority. As for the +10% sensitivity of TSS, there was an unchanged alternatives at rank 1 to 5, for +20% TSS, the unchanged alternative was rank 1 to 2 while in -10% TSS, the unchanged  alternative was rank 1 to 3. Globally, the proposed alternative chosen by the AHP method approach after sensitivity analysis was the first alternative for D60W30P80 and the second alternative for D40W20P80.
Approach of AHP-TOPSIS Method and Sensitivity Analysis. AHP-TOPSIS method combination was chosen in this method because TOPSIS is one type of MCDA that can accommodate real data obtained for priority ranking considerations [7] and different from AHP methods that must fully use expert judgment in determination its decision. In this paper, the combined AHP-TOPSIS method was performed by following division: (i) AHP method for weighting criteria; (ii) TOPSIS method for priority ranking determination. The TOPSIS method used the jar-test data to determine the priority ranking. The following results are presented in Table 3.
Table 5. Priority Ranking used AHP-TOPSIS Method Approach
Priority Ranking
Alternative Variable
CCi+ Value
Rank 1
D60W30P80
0,764
Rank 2
D40W20P80
0,622
Rank 3
D40W30P40
0,583
Rank 4
D40W10P60
0,514
Rank 5
D40W30P80
0,477
Rank 6
D60W10P60
0,473
Rank 7
D60W20P80
0,442
Rank 8
D40W40P40
0,433
Rank 9
D80W20P80
0,413
Rank 10
D80W10P80
0,410
Rank 11
D80W10P60
0,407
Rank 12
D40W40P80
0,320
Rank 13
D60W10P80
0,313
Rank 14
D60W20P60
0,009
The sensitivity analysis used in the AHP-TOPSIS method refers to the weighting of criteria in the AHP method. Based on the analysis of sensitivity to turbidity, there was +10% of unchanged alternative order in rank 1 to 8, turbidity of +20% in rank 1 to 3, turbidity of -10% at rank 1 to 4, TSS of +10% in rank 1 to 4, TSS of +20% in rank 1 to 3 and TSS of -10% in rank 1 to 7. Based on the result of standard approach of the combined AHP-TOPSIS method which tend to be stable after the sensitivity analysis, it was concluded that the first alternative was chosen for D60W30P80, while it was the second alternative for D40W20P80 and third alternative for D40W10P60.
The Comparison of Priority Ranking Between AHP and AHP-TOPSIS Method. The comparison for the approach of these two methods was based on a ranking of priorities that tends to be consistent when given different levels of sensitivity. Based on both methods, the alternative suggestions was generated to be chosen. These alternative suggestions was subsequently compared for two methods and the same ranking alternative were obtained which were the first alternative for D60W30P80 and the second alternative for D40W20P80.
Conclusion
The authors in this study concluded that the use of multicriteria decision analysis (MCDA) method was useful in the selection of several alternative decision results of jar-test with lots of criteria. The selection of MCDA types proposed in this study refers to the subject matter under study, the objectives to be achieved, the existence of  constraint and methods that were able to accommodate the real results of the experiment to be considered in determining the alternative decision. In the process of criteria weighting proposed by expert judgment which has several qualifications that have been required according to their knowledge in steam power plant water treatment. Advanced calculations from experts were assisted using EC 11 software and were proven to assist in the determination of criteria weighting, priority ranking of AHP methods and sensitivity analysis. The software can be used to determine the priority ranking changes if there is a policy change from decision makers that affect the assessment of the criteria weight. The problem addressed in this research was about coagulation-floculation in steam power plant, the approach of AHP method must go through the initial stages which is quite difficult with initial stage is scoring system, while approach method of AHP-TOPSIS still use the data of jar-test result until the determination of rank priority was performed. The proposed final conclusions was selected based on the sensitivity analysis and the different methods used. The conclusion are (i) the first alternatives for D60W30P80 with definitions of 60% dose, 30 minutes residence time and 80 rpm agitator cycle and (ii) the second alternative for D40W20P80 with the definition of 40% dose, residence time of 20 minutes and 80 rpm agitator cycle. The recommendations of this study are to conduct further development of the results obtained in this paper. It is still necessary for further research such as pilot experiment test as a calibration process on the simulation system to improve the validity of the results that have been recommended.
Acknowledgements
The author would like to thank for: Udisubakti Ciptomulyono as supervisor and Endah Angreni as co-supervisor, Kanapi Subur Dwiyanto as my leader in steam power plant, Mesiyah as my beloved mother, Shinta Listyani as my beloved wife, Arqan Neurvagus Feriyanto as my beloved son and Mahira Auruma Feriyanto as my beloved daughter.
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